PENERAPAN SUPPORT VECTOR MACHINE DAN ANALISIS ASOSIASI UNTUK ANALISIS ULASAN APLIKASI E-TICKETING (STUDI KASUS: TIKET.COM)
DOI:
https://doi.org/10.32546/jusin.v7i1.3200Keywords:
E-ticketing, Support vector machine, sentiment analysis, Association Analysis, Tiket.com.Abstract
The development of digital technology has driven the wider adoption of e-ticketing systems on the Tiket.com application, which is increasingly used by the public. However, the increasing number of users doesn't always correlate directly with satisfaction levels, as evidenced by the diverse user reviews on the Google Play Store. This research is urgent for analyzing user sentiment as a basis for improving service quality. The purpose of this research is to classify reviews into positive and negative sentiments using the Support Vector Machine (SVM) algorithm and to analyze dominant word patterns thru the Apriori algorithm. The research data consists of 5,000 Indonesian-language reviews collected between 2023 and 2025, which were then processed thru preprocessing, TF-IDF weighting, SVM classification, association analysis with Apriori, and result visualization using Streamlit. The research results show that SVM produces a high level of accuracy in sentiment classification, while association analysis reveals dominant words that reflect user satisfaction and complaints. The integration of these two methods provides a more comprehensive understanding of user opinions and is expected to serve as a basis for developing service improvement strategies and as a reference for further research in the field of machine learning-based sentiment analysis.
References
Ahmad, R., & Wibowo, M. A. (2021). Pemanfaatan Google Colab sebagai Media Eksperimen Machine learning Berbasis Cloud. Jurnal Teknologi dan Sistem Komputer, 9(2), 132–138. https://doi.org/10.14710/jtsiskom.v9i2.33567
Ainunnisa, I. R., & Sulastri, S. (2023). Analisis Sentimen Aplikasi Tiktok dengan Metode Support vector machine (SVM), Logistic Regression dan Naïve Bayes. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 6(3), 423–430. https://doi.org/10.32493/jtsi.v6i3.31076
Ammar, A., Abdullah, A. H., & Ibrahim, R. (2021). A comparison of Support vector machine kernel functions in sentiment analysis. International Journal of Advanced Computer Science and Applications, 12(1), 308–313. https://doi.org/10.14569/IJACSA.2021.0120139
Arfah, N. R., & Wicaksono, A. (2021). Penggunaan Streamlit untuk Visualisasi Model Klasifikasi Berbasis Data Ulasan Produk E-commerce. Jurnal Teknologi dan Sistem Komputer, 9(4), 243–249. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/10442
Aulia, R., & Mulyadi, A. (2021). Implementasi Visual Studio Code dalam Pengembangan Web Scraping Python. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 8(1), 55–62.
Chatterjee, P., & Dhar, T. (2020). A comparative study of kernel functions in SVM for text classification. Procedia Computer Science, 167, 2161–2168. https://doi.org/10.1016/j.procs.2020.03.265
Fide, S., Suparti, S., & Sudarno, S. (2021). Analisis Sentimen Ulasan Aplikasi Tiktok Di Google Play Menggunakan Metode Support vector machine (Svm) Dan Asosiasi. Jurnal Gaussian, 10(3), 346–358. https://doi.org/10.14710/j.gauss.v10i3.32786
Fitriyani, I., & Haris, M. (2021). Analisis Kinerja NumPy dan Pandas dalam Proses Transformasi Data Berbasis Python. Jurnal Informatika dan Sistem Informasi, 6(2), 90–98.
Forest, R., & Smote, D. (2024). Hal. 508-513 Vol. 6; No. 3. 508–513.
Fortune Indonesia. (2021). Pariwisata Mulai Pulih, Kinerja tiket.com Turut Bertumbuh. Diakses dari https://www.fortuneidn.com
Handayani, P. W., Hidayanto, A. N., & Ayuningtyas, D. (2018). Critical success factors for electronic health record system implementation: A case study in Indonesia. Heliyon, 4(11), e01089. https://doi.org/10.1016/j.heliyon.2018.e01089
Hendrawan, A. T., & Prasetya, R. A. (2021). Penerapan Algoritma Apriori pada Ulasan Produk Menggunakan Mlxtend. Jurnal Ilmu Komputer dan Aplikasi, 8(3), 201–208.
Kurniawan, A. S., & Pratama, I. (2025). Analisis Sentimen Review Aplikasi Jogja Smart Service pada Google Play Store Menggunakan Metode SVM Sentiment Analysis of Jogja Smart Service Application Reviews On Google Play Store Using SVM. 14(105), 130–141.
Ma’ruf, A. (2019). Sistem Informasi Manajemen Rekam Medis Elektronik. Jurnal Teknologi dan Sistem Informasi, 3(1), 12-20.
MIX Marcomm. (2023). Minat Wisata Kian Menguat, Transaksi Pengguna tiket.com Meningkat 20%. Diakses dari https://mix.co.id
Muttaqin, M. N., & Kharisudin, I. (2021). Analisis Sentimen Pada Ulasan Aplikasi Gojek Menggunakan Metode Support vector machine dan K Nearest Neighbor. UNNES Journal of Mathematics, 10(2), 22–27. http://journal.unnes.ac.id/sju/index.php/ujm
Nofandi, A., Setiawan, N. Y., & Brata, D. W. (2023). Analisis sentimen ulasan pelanggan dengan Metode Support vector machine (SVM) untuk peningkatan kualitas layanan pada Restoran Warung Wareg. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(1), 458–466. https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/12218
Nurfadillah, S. (2022). Pengembangan sistem informasi rekam medis berbasis web pada Puskesmas Desa Cemerlang. Jurnal Teknik Informatika, 11(1), 33–40.
Purnomo, H. (2020). Evaluasi sistem informasi rekam medis elektronik menggunakan pendekatan Technology Acceptance Model (TAM). Jurnal Penelitian Komputer dan Sistem Informasi, 8(2), 112–119.
Rahmat, I. (2021). Analisis dan Perancangan Sistem Informasi Rekam Medis pada Klinik X dengan Metode Waterfall. Jurnal Sistem dan Teknologi Informasi, 9(2), 154–162.
Ramadhan, A. (2021). Perancangan aplikasi rekam medis berbasis website menggunakan framework Laravel. Jurnal Teknologi dan Sistem Komputer, 9(3), 210–218.
Saragih, H. (2019). Implementasi sistem rekam medis berbasis web di Puskesmas XYZ menggunakan metode Waterfall. Jurnal Sistem Informasi, 7(1), 34–4
Savitri, N. (2019). Perancangan sistem informasi rekam medis menggunakan metode prototype di Puskesmas. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), 6(4), 789–796.
Syamsudin, R., & Fadillah, R. (2023). Implementasi GUI Interaktif Menggunakan Streamlit dalam Sistem Prediksi Penyakit Menggunakan Algoritma Klasifikasi. Jurnal Informatika dan Sistem Informasi (JISI), 10(2), 115–122. https://doi.org/10.31294/jisi.v10i2.34119
Widi Hastomo, I., Suhartono, A., & Nugroho, H. (2022). Pengembangan Aplikasi Prediksi Menggunakan Streamlit untuk Visualisasi Data Machine Learning. Jurnal Ilmiah Teknologi dan Informatika (JUTI), 5(1), 89–96. https://doi.org/10.26798/juti.v5i1.3271
Yadav, R., & Vishwakarma, D. K. (2022). Sentiment analysis using improved SVM and RBF kernel for social media data. Multimedia Tools and Applications, 81, 16407–16427. https://doi.org/10.1007/s11042-021-11699-5
Yolanda, A. M., & Mulya, R. T. (2024). Implementasi Metode Support vector machine untuk Analisis Sentimen pada Ulasan Aplikasi Sayurbox di Google Play Store. 6(2), 76–83. https://doi.org/10.35580/variansiunm25
